Urinary Microbiome Characteristics in Kidney Transplant Recipients and Their Clinical Implications: A Narrative Review

Introduction

EVIDENCE BASE AND SEARCH STRATEGY:

We searched PubMed and Google Scholar (2013–2026) using combinations of “kidney transplantation”, “urinary microbiome”/”urobiome”, “metagenomics”, “16S rRNA”, “urinary tract infection”, “acute rejection”, and “interstitial fibrosis”. Priority was given to longitudinal cohorts, clinically adjudicated endpoints, and multi-omics analyses. Reference lists of recent systematic reviews were screened for additional studies.

Baseline Urinary Microbiome in Healthy Individuals

THE URINARY MICROBIOME IN HEALTHY INDIVIDUALS:

In healthy individuals, the urinary microbiome exhibits pronounced sex-related differences, which directly influence post-transplant risk stratification and prognostic assessment. The urinary microbiome is characterized by an intrinsically low biomass compared with other anatomical sites; however, its marked inter-individual variability provides a valuable basis for risk stratification in transplant medicine. Genomic analyses by Zhang et al demonstrated that the secondary metabolite biosynthetic gene clusters in Lactobacillus species predominantly encode antagonistic bacteriocins. These protective functional molecules are essential for sustaining urinary tract homeostasis and preventing overgrowth of opportunistic pathogens [23]. The urinary microbiome has a substantially lower biomass than that of the gut and other body sites, with the microbial load in females generally estimated at approximately 104–105 CFU/mL. Studies of the urinary microbiome in healthy individuals aged 26–90 years have revealed age-associated microbial shifts, showing that nearly two-thirds of detected bacterial taxa cannot be identified using conventional culture-based methods [24]. Transplant recipients are typically older, and their baseline urinary microbiome profiles may differ substantially from those of younger healthy individuals. In healthy populations, females exhibit markedly greater microbial richness and diversity than males. Although this sex-related disparity shows broadly comparable patterns at the phylum level, pronounced divergence becomes evident at the genus level.

IMPLICATIONS FOR TRANSPLANT MEDICINE:

Although the urinary microbiome of healthy individuals demonstrates substantial inter-individual variability, a definable “core” microbial community persists, and the functional attributes of these core taxa provide important reference points for post-transplant monitoring. The key baseline characteristics of the urinary microbiome in healthy individuals, together with their potential clinical implications for transplant medicine, are summarized in Table 1. Age exerts a significant influence on community structure; in particular, older women have increased microbial diversity. Age, menopausal status, prior history of urinary tract infection, and host genetic factors are recognized as major determinants shaping the composition of the urinary microbiome [16]. This observation underscores the necessity of accounting for baseline factors such as age and sex when evaluating microbiome alterations in transplant recipients. In a large male cohort, Karstens et al reported that BMI and benign prostatic hyperplasia were closely associated with urinary microbiome diversity and lower urinary tract symptoms, further supporting the regulatory influence of host physiological status on microbial community composition [25]. In the context of transplant medicine, these findings suggest that metabolic syndrome and underlying urological conditions indirectly influence transplant outcomes by altering the baseline urinary microbiome. In females with acute urinary incontinence, the urinary microbiome exhibits characteristic shifts, with reduced microbial diversity showing a negative correlation with symptom severity [26].

Establishing baseline microbiome characteristics provides a foundation for developing individualized post-transplant management strategies. Although the Human Microbiome Project did not include the urinary tract, advances in expanded quantitative urine culture (EQUC) and molecular sequencing technologies have fundamentally overturned the long-held assumption that urine is sterile [27]. This paradigm shift carries transformative implications for transplant medicine, offering a novel framework for understanding the underlying mechanisms driving post-transplant complications.

The urinary microbiome, as a potential biomarker and therapeutic target, has already demonstrated diagnostic and prognostic utility in conditions such as bladder cancer [28]. In transplant medicine, characterizing baseline microbiome profiles enables prediction of post-transplant risks – including infection, rejection, and chronic complications – based on each recipient’s microbial signature. Such understanding supports the development of individualized pre-transplant risk assessment systems; informs perioperative management decisions, including the selection of prophylactic antibiotics and the optimal timing of probiotic interventions; and provides novel monitoring indicators for long-term follow-up and early detection of transplant-related complications.

Determinants Shaping the Post-Transplant Urobiome

HOST-RELATED FACTORS:

The developmental trajectory of the urinary microbiome parallels the physiological maturation of the host. In early life, the microbial community is relatively simple and shows minimal sex-related divergence. A study of 85 children under 48 months demonstrated that Prevotella, Peptoniphilus, Escherichia/Shigella, Veillonella, and Finegoldia constitute the foundational microbial framework during childhood, with no significant differences observed between males and females [29]. This early sex-neutral pattern reflects the largely stochastic nature of microbial colonization before the maturation of hormonal regulation. As children enter the preschool stage, sex-related divergence becomes increasingly evident. In healthy preschoolers, girls show a predominance of Prevotella (18.2%), Porphyromonas (12.9%), and Ezakiella (8.1%), whereas in boys, Porphyromonas is most abundant (22.4%), followed by Ezakiella (12.0%) and Campylobacter (11.6%) [30]. This pattern of divergence suggests that anatomical differences increasingly shape microbial colonization. Puberty is a critical turning point in microbiome development. Evidence indicates that prepubertal females exhibit greater genus-level diversity, whereas post-pubertal females show a reduction in diversity, with Lactobacillus emerging as the dominant genus [31]. This transition reflects the establishment of estrogen-driven selective pressures on microbial colonization, thereby shaping the foundational microbiological landscape that underpins urogenital health in adulthood.

In older adults, the urinary microbiome undergoes shifts that contrast with developmental patterns, transitioning from a relatively stable, dominant community structure toward increased diversity. Evidence from large twin cohort studies indicates that age, menopausal status, prior urinary tract infection history, and host genetic background are key determinants of urinary microbiome composition. The observed rise in microbial diversity is frequently associated with advancing age [16]. In older women, an Escherichia coli–dominant community type is more commonly associated with healthy aging, whereas a Gardnerella–dominant profile is more characteristic of younger women [8]. This pattern reflects a redistribution of microbial ecological niches driven by age-related declines in host defense capacity.

The influence of sex on the urinary microbiome extends beyond differences in anatomical structure. The shorter female urethra, together with its close proximity to the genital and gastrointestinal tracts, renders the female urinary microbiome more susceptible to external environmental influences [32]. At the genus level, females exhibit significantly greater microbial richness and diversity in the urinary microbiome compared with males [15]. At the phylum level, the urinary microbiome of healthy adult men and women is broadly similar, with most detected taxa belonging to Firmicutes (65% in males vs 73% in females) [8]. Most genera present in the urinary microbiome are shared between males and females, including Prevotella, Escherichia/Shigella, Enterococcus, Streptococcus, and Citrobacter. Pseudomonas, however, has been detected exclusively in males [33]. The presence of such sex-specific genera highlights the influence of distinct physiological environments on selective microbial colonization.

Hormonal regulation is a fundamental driver of sex-related differences in the urinary microbiome. The marked decline in estrogen levels during menopause leads to increased α-diversity and a reduced proportion of Lactobacillus in urine – changes that precede the development of recurrent cystitis [34]. In postmenopausal women, diminished estrogen exposure contributes to urogenital symptoms, and accompanying shifts in the urinary microbiome can influence bladder physiology and increase vulnerability to urinary tract symptoms [35]. The urinary microbiome of postmenopausal women with recurrent urinary tract infections differs markedly from that of age-matched controls, with the presence of anaerobic taxa being particularly associated with recurrent infections in this population [36].

ENVIRONMENTAL AND TREATMENT-RELATED FACTORS:

Chronic diseases influence the urinary microbiome by altering the host’s internal milieu. In patients with chronic kidney disease, progressive decline in renal function leads to changes in urine composition and excretory patterns, accompanied by a marked reduction in microbial diversity. Kidney transplantation reverses some of these CKD-related alterations; however, under the combined pressures of immunosuppression and antibiotic exposure, transplant recipients typically exhibit decreased microbial diversity together with an increased abundance of opportunistic pathogens [11]. In transplant recipients, factors such as the degree of HLA matching, ABO incompatibility, and delayed graft function are not only associated with post-transplant outcomes but also contribute to shaping the composition of the urinary microbiome [20]. The urinary microbiome of kidney transplant recipients is closely linked to graft immune status. Patients who achieve spontaneous immune tolerance have a highly diverse microbial community with a distinct enrichment of Proteobacteria [10].

Underlying metabolic conditions such as diabetes exert a substantial impact on urinary microbiome composition. Compared with healthy controls, individuals with diabetes display reduced microbial richness and lower α-diversity within the urinary community [37]. Diabetic individuals also have a higher overall bacterial load in urine, along with an increased abundance of taxa belonging to the phylum Firmicutes, compared with healthy controls [38]. These alterations are not merely consequences of disease but may also contribute to its progression. Antibiotic exposure is one of the most potent environmental forces shaping the urinary microbiome. In kidney transplant recipients, prophylactic antibiotic regimens exert a measurable impact on microbial community structure. Patients receiving trimethoprim–sulfamethoxazole prophylaxis demonstrate a marked reduction in Actinobacteria at the phylum level, alongside an increase in Firmicutes, driven largely by the expansion of Enterococcus. Such antibiotic-induced shifts facilitate the emergence of antimicrobial resistance within the urinary microbiome. Metagenomic functional analyses of urine have shown increased enzyme abundance within folate-metabolism pathways, including dihydropteroate synthase – an enzyme not inhibited by trimethoprim–sulfamethoxazole but capable of enhancing folate biosynthesis [11]. Immunosuppressive agents exert distinct modulatory effects on the urinary microbiome. Members of the Streptococcaceae family show a negative association with mTOR inhibitors, whereas Staphylococcaceae are negatively correlated with calcineurin inhibitors [10]. Among kidney transplant recipients receiving various immunosuppressive regimens, the urinary microbiome exhibits pronounced differences at the level of functional gene profiles [39]. Such regimen-specific interactions between immunosuppressive agents and the urinary microbiome may offer valuable microbiological guidance for tailoring immunosuppressive therapy on an individualized basis.

INTERPLAY AMONG HOST, ENVIRONMENT, AND MICROBIOTA:

Surgical transplantation and its associated environmental alterations exert multifactorial influences on the urinary microbiome. Following transplantation, recipients are exposed to a constellation of stressors, including surgical trauma, perioperative immunosuppression, prophylactic and therapeutic antibiotic administration, and shifts in the surrounding environment [20]. Transplant-related complications also influence the urinary microbiome. Patients with chronic allograft dysfunction exhibit a higher prevalence of Corynebacterium species [40]. Interstitial fibrosis and tubular atrophy are associated with shifts in resident urinary microbiota and an increased presence of pathogenic organisms [22]. The microbiome not only reflects underlying disease processes but may also act as a driver of disease progression. Evidence indicates that the urinary microbiome forms an individual-specific microbial fingerprint shaped jointly by host and environmental factors.

Traditional studies on post-transplant complications have largely focused on immunologic and pharmacologic mechanisms, while the role of the urinary microbiome has only recently gained attention. High-throughput sequencing has driven this shift in understanding, revealing that urinary microbiome dysbiosis is not merely an accompanying phenomenon but is also an integral pathogenic component of transplant-related complications. This paradigm marks a transition from a “single-pathogen invasion” model to a broader concept of “microecological imbalance.”

EPIDEMIOLOGY AND MICROBIOME ASSOCIATIONS: Urinary tract infection (UTI) is the most common infectious complication after kidney transplantation, and its epidemiologic features have been extensively described. From a microbiome perspective, however, emerging analyses are reshaping current understanding. The incidence of UTI in kidney transplant recipients can reach 60%, with 10–15% progressing to recurrent UTI, which markedly affects graft outcomes [41–43]. However, the microbial mechanisms underlying these observations have only begun to be elucidated in recent years.

Based on data from 72 600 patients, the combined incidence of UTI in kidney transplant recipients is 35%, and the risk in female recipients is 3.13 times that of males [21]. Recurrent UTI exhibits distinct temporal and etiologic patterns. Approximately 72% of UTI cases are recurrent, and 73.6% occur within the first year after transplantation, indicating that the early post-transplant period is a critical window for UTI prevention and management [11]. High-throughput sequencing-defined microbiome alterations provide a new framework for understanding shifts in the pathogenic spectrum of UTI. Conventional culture methods indicate that Escherichia coli remains the predominant pathogen, but the proportions of opportunistic organisms such as Klebsiella pneumoniae, Pseudomonas aeruginosa, and Enterococcus faecium have increased substantially. Metagenomic sequencing has shown that among 21 kidney transplant recipients, 11 patients with Enterococcus abundances exceeding 10% subsequently developed UTI. High levels of dihydrofolate synthase genes were detected, suggesting potential resistance to trimethoprim–sulfamethoxazole [12,44]. These findings support the notion that UTI arises not only from exogenous pathogen invasion but more importantly from “opportunistic breakthroughs” of colonizing microbes under conditions of microecological imbalance. To integrate current evidence, key urinary microbiome signatures linked to UTI occurrence, recurrence, and antimicrobial resistance in kidney transplant recipients are synthesized in Table 2.

Comparative analyses of urinary bacterial community structures in kidney transplant recipients and dialysis patients reveal marked differences between the 2 groups, with transplant recipients exhibiting distinct microbial profiles [45]. During acute kidney injury, kidney transplant recipients display a bacterial genus distribution in the urinary microbiome that differs from that of non-transplant patients, and changes in microbial α-diversity indices can predict the risk of infection [46]. Longitudinal monitoring studies further show that characteristic microbial alterations occur 2–7 days before the onset of clinical UTI symptoms, including a sharp decline in diversity, an increase in opportunistic pathogens, and a simplification of microbial interaction networks [47]. Studies examining the association between specific urinary metabolites, microbiome status, and UTI risk have revealed functional insights at the microbial level. Within the urine–gut–bladder axis, microbial metabolites such as short-chain fatty acids play key regulatory roles [48]. Studies of the tryptophan metabolic pathway have shown that metabolites such as indole-3-acetic acid (IAA) and indole-3-propionic acid (IPA) are positively correlated with the abundance of Lactobacillus species and negatively correlated with the risk of UTI [49]. Reduced levels of short-chain fatty acids, particularly butyrate and propionate, indicate functional microbial disruption and an increased risk of UTI [50,51]. Host defense peptides such as β-defensin 2 (hBD-2) and catecholamine levels are closely associated with the maintenance of microbial diversity and the prevention of UTI [52]. These findings confirm that these metabolites play essential roles in maintaining gut homeostasis and regulating immune responses.

PATHOGENIC MECHANISMS AND MICROBIOME-MEDIATED DEFENSE: Microbiome research has revealed that the pathogenic mechanisms underlying UTI are far more complex than the traditional “single-pathogen invasion” model. Contemporary concepts view UTI as a consequence of microecological imbalance, characterized not only by shifts in microbial composition and abundance but also by the loss of protective functions of commensal bacteria and the enhanced pathogenicity of opportunistic organisms. Commensal bacteria such as Lactobacillus maintain colonization resistance through multiple mechanisms, preventing opportunistic pathogens from establishing infection. They compete for nutrients and adhesion sites, thereby blocking pathogenic colonization [53]. In vitro co-culture experiments have demonstrated that Lactobacillus crispatus can markedly inhibit the adhesion of Escherichia coli and Klebsiella species to urothelial cells, with inhibition rates reaching 60–80% [54]. Transplant model studies show that pre-existing microbial communities provide colonization resistance, preventing the establishment of exogenous bacterial strains [55]. Commensal bacteria produce multiple bioactive substances that inhibit the growth of opportunistic pathogens. Lactobacillus species generate lactic acid, maintaining an acidic urinary environment (pH <4.5) that suppresses the growth of most opportunistic organisms [56]. Commensal bacteria produce bacteriocins such as lactacin and rhamnolipids, which exert direct bactericidal activity against Enterobacteriaceae. Bifidobacterium and Streptococcus species produce hydrogen peroxide, creating an oxidative environment unfavorable to anaerobic bacteria [9,22].

By modulating biofilm composition, the microbiome influences the colonization capacity of opportunistic pathogens. Biofilms formed by a healthy microbial community exhibit dense architecture and extracellular polysaccharide matrices that favor the growth of commensal bacteria. In a state of microbiome dysbiosis, the biofilm becomes structurally loose, allowing opportunistic pathogens to penetrate more easily and establish localized microenvironments. Butyrate produced by commensal bacteria can downregulate the expression of type III secretion system and flagellar genes in opportunistic pathogens such as Escherichia coli, thereby reducing their virulence [40,57].

The microbiome enhances resistance to UTI through immune regulatory networks. Commensal bacteria interact with urothelial cells to induce moderate Toll-like receptor activation, maintaining a state of host defensive readiness without triggering excessive inflammation. D-lactate produced by Lactobacillus can induce β-defensin expression, strengthening the mucosal immune barrier [18,58]. Variant lipopolysaccharides secreted by commensal bacteria can competitively bind to TLR4, suppressing the excessive inflammatory responses triggered by pathogenic LPS [59]. A healthy microbiome maintains iron homeostasis, limiting the ability of opportunistic pathogens to acquire this essential growth factor. Commensal bacteria degrade urea to produce ammonia, creating an alkaline microenvironment that suppresses the growth of opportunistic pathogens adapted to acidic conditions. Microbial metabolites such as indole derivatives can inhibit quorum-sensing systems in opportunistic pathogens, thereby reducing their virulence expression [60–62].

MICROBIOME ALTERATIONS IN CHRONIC ALLOGRAFT NEPHROPATHY: The core pathological features of chronic allograft nephropathy – interstitial fibrosis and tubular atrophy (IF/TA) – have traditionally been attributed to immune-mediated injury, drug toxicity, and donor-related factors. However, increasing evidence suggests that the urinary microbiome can contribute to the development and progression of chronic allograft dysfunction [63,64].

Studies examining the urinary microbiome in relation to IF/TA progression reveal a time-dependent pattern. A longitudinal study by Modena et al involving 25 patients who developed IF/TA, 23 transplant recipients with stable graft function, and 20 healthy controls demonstrated that early post-transplant (3 months) reductions in microbial diversity were associated with an increased risk of IF/TA, and this association remained an independent predictor even after adjusting for traditional risk factors [65,66]. More refined temporal analyses showed that at 1 month after transplant, microbiome differences between the IF/TA group and the stable-function group were minimal, but these differences became markedly pronounced by 6–8 months. Among female recipients, Lactobacillus dominated in healthy controls but was significantly reduced in the IF/TA group; among male recipients, Streptococcus was the dominant genus in healthy controls, whereas its abundance progressively declined in the IF/TA group and dropped further at 6–8 months [2].

IF/TA patients exhibit a distinct microbiome pattern referred to as a “microbiome risk signature.” This signature is characterized by a depletion of dominant commensals (such as Lactobacillus and Prevotella), an overrepresentation of opportunistic pathogens, and a simplified microbial interaction network [18]. Patients with chronic allograft dysfunction show a significantly increased urinary abundance of Corynebacterium species (P=0.0005), a finding that remains robust after adjustment for sex and other confounders [54]. Multi-omics integrative analyses indicate that microbiome alterations precede histopathological changes and declines in graft function, suggesting that they could serve as early biomarkers of IF/TA. Microbial profiling of transplant kidney biopsy specimens has revealed detectable bacterial DNA within IF/TA tissues, particularly members of the Enterobacteriaceae, Pseudomonadaceae, and Enterococcaceae families [18,67]. Single-cell RNA sequencing and spatial transcriptomic analyses show that these microbe-associated molecular patterns localize to tubular epithelial cells and interstitial macrophages, colocalizing with pro-fibrotic gene expression signatures.

SYSTEMS-LEVEL INSIGHTS INTO MULTIDIMENSIONAL INJURY NETWORKS:

Mechanistic evidence directly linking urinary microbial signals to intragraft injury in kidney transplantation remains limited. Current hypotheses are supported mainly by observational urobiome studies and complementary evidence from non-transplant kidney injury models. The mechanisms outlined below are proposed pathways requiring further experimental validation.

The molecular mechanisms through which the microbiome influences IF/TA progression are diverse and multifaceted, encompassing immune modulation, metabolic perturbations, and direct tissue injury, collectively forming an interconnected multidimensional injury network.

Microbiome dysregulation activates multiple immune pathways, promoting chronic inflammation and fibrosis. Lipopolysaccharides from opportunistic pathogens trigger the TLR4 signaling cascade, activating NF-κB and MAPK pathways and inducing proinflammatory cytokine production in tubular epithelial cells [68]. These cytokines recruit monocytes and macrophages into the renal interstitium, where they differentiate into pro-fibrotic M2 macrophages that secrete fibrogenic mediators such as TGF-β and PDGF [69]. Microbiome dysregulation disrupts the regulatory T cell/Th17 balance, shifting the immune milieu toward a proinflammatory and pro-fibrotic state. Single-cell RNA sequencing has shown that specific microbial metabolites, such as indole-3-aldehyde, can modulate dendritic cell function through the aryl hydrocarbon receptor, thereby affecting antigen presentation and T cell differentiation [52,70].

Microbial metabolites influence graft tissue homeostasis through multiple pathways, forming a direct mechanistic link from metabolic disruption to tissue injury. Short-chain fatty acids can inhibit histone deacetylases, thereby regulating the expression of pro-fibrotic genes [71,72]. Microbially derived uremic toxins directly injure tubular epithelial cells, promoting phenotypic transition and fibrosis [73]. Alterations in the urea–nitrogen cycle lead to the accumulation of methylated metabolites, which are associated with tubular injury and fibrotic progression [74]. Metabolomic studies indicate that dysregulation of microbiome-mediated tryptophan metabolism correlates with the rate of IF/TA progression, and the tryptophan/kynurenine ratio could serve as a predictive biomarker [75].

Microbial factors can promote epithelial–mesenchymal transition (EMT) in tubular epithelial cells, a key event in the development of IF/TA [76,77]. Proteases secreted by opportunistic pathogens, such as elastases and metalloproteinases, can degrade components of the basement membrane and initiate EMT [78]. Microbial metabolites such as uremic toxins can activate the Wnt/β-catenin and TGF-β/Smad signaling pathways, inducing downregulation of the epithelial marker E-cadherin and upregulation of mesenchymal markers such as α-SMA, fibronectin, and collagen I [58]. Certain commensal-derived metabolites can suppress TGF-β–induced EMT, conferring a potential protective effect [54]. Microbiome dysregulation exacerbates oxidative stress and autophagy disturbances in the transplanted kidney, creating a vicious cycle of disrupted cellular homeostasis. Lipopolysaccharides and flagellin produced by opportunistic pathogens activate NADPH oxidase, increasing the generation of reactive oxygen species [79]. Reactive oxygen species (ROS) further impair mitochondrial function, perpetuating a vicious cycle of cellular damage. Microbial metabolites interfere with antioxidant systems, reducing the host’s antioxidant defense capacity [80,81]. Microbial factors modulate autophagy by regulating the mTOR and AMPK signaling pathways, and autophagy dysregulation is closely associated with IF/TA progression [82]. Metabolomic analyses have shown that microbiome dysregulation is associated with mitochondrial dysfunction and elevated oxidative stress markers in graft tissue [83].

Fluorescence in situ hybridization and dual immunofluorescence labeling studies suggest that certain bacteria, such as Escherichia coli, can directly invade tubular epithelial cells and form intracellular colonies, triggering pyroptosis and necroptosis [84]. Bacterial DNA detected in transplant kidney tissue correlates with tissue-specific transcriptomic patterns, particularly those associated with innate immunity and tissue repair [85]. Electron microscopy has confirmed that following tubular obstruction and ischemia–reperfusion injury, bacteria can traverse the disrupted tubular basement membrane and enter the renal interstitium. Although these findings require further validation, they suggest that microbes may directly contribute to graft injury [59,86].

UROBIOME AND ACUTE REJECTION:

The causal relationship between urobiome alterations and acute rejection has not been definitively established. Observational studies demonstrate temporal associations, but whether microbiome changes directly trigger rejection or are secondary consequences of immune activation remains unclear. The framework below synthesizes correlative evidence from clinical studies, murine models, and in vitro assays.

Research on the mechanisms of acute rejection is shifting from traditional immunologic models toward a microbiome–immune interaction framework. Emerging evidence indicates that alterations in the gut microbiome can predict the occurrence of transplant rejection [87,88].

Reduced urinary microbial diversity is significantly associated with an increased risk of acute rejection [89]. Time-series analyses by Holle et al showed that patients with abnormal ratios of specific bacterial taxa in the gut and urine during the early post-transplant period (1–4 weeks) had a significantly increased risk of developing acute rejection within the subsequent 90 days [90,91]. This association remained statistically significant after adjustment for traditional risk factors, suggesting that the microbiome could serve as an independent predictor of rejection [92].

Lipopolysaccharides produced by opportunistic pathogens act as microbial-associated molecular patterns that activate dendritic cells through Toll-like receptor 4, enhancing their alloantigen-presenting capacity and upregulating costimulatory molecule expression [93]. Integrated analyses of the microbial metagenome and host transcriptome have revealed the complexity of the “microbiome–host” interaction network. Microbiome dysregulation not only directly activates innate immunity but also modulates the host metabolic environment, thereby influencing T cell function [94]. Specifically, microbiome dysregulation leads to reduced short-chain fatty acids, thereby impairing T cell energy metabolism and regulatory T cell differentiation, ultimately disrupting immune tolerance [95].

Cross-reactivity between microbial antigens and alloantigens provides a novel explanatory dimension for the mechanisms of rejection. T cell receptor sequencing and epitope analyses have confirmed that certain microbial proteins share structural similarity with donor HLA molecules, thereby triggering cross-reactive T cell responses [96]. Patients receiving long-term antibiotic therapy exhibit a significantly lower incidence of rejection compared with controls [97]. Although antibiotics can influence the immune system through multiple pathways, this observation reinforces the hypothesis that the microbiome contributes to development of rejection. Further clinical evidence comes from observations of dynamic microbiome changes before and after rejection episodes. At 2–7 days before the onset of rejection, characteristic alterations emerge in the urinary microbiome, including a sharp decline in α-diversity and a rapid expansion of opportunistic pathogens [98]. These alterations precede conventional rejection markers and histologic changes, suggesting that the microbiome could serve as an early warning signal for rejection. Intensified immunosuppressive therapy after rejection induces further microbiome dysregulation, perpetuating a vicious cycle. This observation suggests that protective microbiome-targeted strategies should be considered during rejection treatment to interrupt this cycle.

Concurrent analyses of the gut and urinary microbiomes reveal a strong correlation between them, and their coordinated alteration patterns provide greater predictive accuracy for rejection than either microbiome alone [99]. This finding supports the concept of a “gut–kidney immune axis”, in which the gut microbiome influences the immune environment of the transplanted kidney through both modulation of systemic immunity and microbial translocation. Further studies show that impaired gut barrier function is closely associated with microbial translocation and an increased risk of rejection, suggesting that maintaining gut health may be a potential strategy for rejection prevention [100]. Different types of rejection may be associated with distinct microbiome profiles, with each rejection subtype exhibiting its own unique microbial “fingerprint”.

In patients with TCMR, the proportions of opportunistic pathogens – particularly Escherichia coli and Pseudomonas aeruginosa – are markedly higher in the urine [101]. Metagenomic functional analyses reveal increased abundances of genes involved in LPS biosynthesis and flagellar assembly in the microbiomes of TCMR patients, and these gene products can directly activate the innate immune system [102]. These findings are consistent with the pathophysiologic mechanisms of TCMR, which are dominated by T cell–mediated cytotoxicity and delayed-type hypersensitivity responses. More detailed functional analyses show that the metabolic capacities of the microbiome are markedly altered in TCMR, whereas antibody-mediated rejection (ABMR) is associated with a distinct microbiome pattern. In ABMR patients, the urine shows increased proportions of anaerobes such as Clostridium and Peptostreptococcus, accompanied by a relative decrease in aerobic bacteria [2]. ABMR patients have increased abundances of genes involved in capsular polysaccharide biosynthesis, which are structures known to trigger antibody production [18]. Cross-reactivity between antimicrobial antibodies and donor-specific antibodies has been observed in patients with ABMR. This finding provides direct evidence that the microbiome can influence ABMR and helps explain why some patients develop donor-specific antibodies despite lacking a clear sensitization history [20].

Subclinical rejection is a distinct form of rejection characterized by histologic evidence of allograft injury despite normal serum creatinine levels. Patients with subclinical rejection exhibit a moderately reduced urinary microbial diversity and a modest increase in opportunistic pathogens, although to a lesser extent than in clinical rejection [67]. Leveraging this characteristic, a microbiome-based signature was developed that, when combined with traditional monitoring indicators, improves the detection rate of subclinical rejection by approximately 35%. This finding has important clinical implications, as early identification and treatment of subclinical rejection can significantly improve long-term outcomes. Interventional studies have shown that early correction of microbiome dysregulation–such as through probiotic supplementation–can reduce the risk of subclinical rejection progressing to clinical rejection [57,103].

Mixed rejection, which exhibits features of both TCMR and ABMR, is associated with the most profound microbiome dysregulation. Multi-omics integrative analyses show that patients with mixed rejection have the lowest microbial diversity, the highest proportion of opportunistic pathogens, and the most simplified microbial interaction networks [104]. Multiple pathogenic pathways are concurrently activated, including Toll-like receptor signaling, complement cascades, and antigen presentation pathways. These findings suggest that microbiome dysregulation can act as a “trigger factor” for mixed rejection, simultaneously activating multiple immune pathways and leading to more severe allograft injury [105].

Biomarkers and Interventions

BIOMARKER DEVELOPMENT: CURRENT STATUS AND FUTURE PROSPECTS:

Recent studies indicate that alterations in the gut microbiome can be detected prior to the onset of graft rejection, providing a critical foundation for the development of microbiome-based predictive models [106]. In a multicenter prospective study involving 562 samples, Holle et al found that patients exhibited reduced gut microbial diversity and a decline in short-chain–fatty-acid–producing bacteria prior to rejection. The study further confirmed, through functional analyses, a marked reduction in short-chain-fatty-acid–producing capacity prior to rejection, and quantitative PCR validated the decreased abundance of propionate- and butyrate-producing bacteria. Post-rejection microbiome profiles tended to normalize, and the study by Holle et al similarly concluded that these microbiome changes are directly associated with rejection events rather than being secondary alterations [2]. This finding provides important support for the microbiome as a predictive biomarker, although validation in larger independent cohorts is still required. By integrating microbiome and metabolomic data, unique patterns of specific bacterial species and metabolites have been identified in the excreta of patients with antibody-mediated rejection (AMR) [107]. This combined model demonstrated high diagnostic performance in preliminary studies, with an area under the ROC curve exceeding 0.9. As with other biomarker research, these findings require further validation and investigation in larger and more diverse patient populations [108].

Analyses of the microbiome in kidney transplant recipients have revealed that specific microbial signatures are closely associated with graft rejection, immunosuppressant metabolism, and post-transplant diabetes mellitus [18]. Alterations in gut microbial composition are associated with the development of new-onset diabetes after transplantation in kidney recipients [109]. A metagenomic sequencing study by Swarte et al involving 507 kidney transplant recipients showed that the gut microbiome is closely associated with health-related quality of life, with physical and mental health scores explaining 0.58% and 0.37% of microbiome variability, respectively [106]. Although this association is statistically significant, its clinical relevance requires further evaluation.

Beyond microbial composition, urinary host biomarkers may complement microbiome profiling for rejection surveillance. Urinary CXCL10 is elevated during T cell- and antibody-mediated rejection episodes [110]. Integration of urobiome signatures with molecular markers such as CXCL10 may enhance early detection sensitivity, but prospective validation in large cohorts is needed.

Artificial intelligence and machine learning approaches have shown potential in the development of microbiome-based biomarkers. The application of deep learning algorithms to microbiome time-series data has significantly improved predictive accuracy [111]. Ensemble learning approaches can integrate multiple data types – including microbiome, metabolomic, transcriptomic, and clinical parameters – providing a new avenue for developing comprehensive predictive systems [112]. However, establishing standardized sample collection protocols is crucial for improving the generalizability of such research findings [113].

MICROBIOME-TARGETED INTERVENTION STRATEGIES:

Long-term prophylactic antibiotic use can cause sustained disruption of the microbiome [19]. While effectively preventing infection, minimizing broad disruption of commensal communities helps maintain microbiome stability and diversity. When antimicrobial therapy is necessary, a “narrow-spectrum first” strategy – prioritizing agents with high specificity for the target pathogen – can reduce the extent of microbiome disruption [18,114]. Multiple clinical trials have confirmed the safety and potential efficacy of probiotics in kidney transplant recipients [115]. A review by Ye et al showed that probiotic therapy can reduce the incidence of urinary tract infections and improve gut barrier function [116]. Synbiotic formulations containing Lactobacillus and Bifidobacterium species significantly reduce plasma p-cresol levels and improve the clearance of uremic toxins [117]. In 27 kidney transplant recipients with gastrointestinal symptoms, treatment with a prebiotic powder suspension for 7 weeks led to significant symptom relief [118].

Short-chain–fatty-acid–based therapies improve graft function by promoting regulatory T cell differentiation and reducing inflammation. A high-fiber diet increases the abundance of short-chain–fatty-acid–producing bacteria, prolongs graft survival, and reduces rejection events [119]. Phage-based therapies can selectively eliminate resistant bacteria while preserving beneficial microbial communities, offering a novel approach to addressing antibiotic resistance [120].

METHODOLOGICAL CHALLENGES IN LOW-BIOMASS UROBIOME STUDIES:

Urinary samples present unique challenges due to low bacterial loads (102–104 CFU/mL), rendering analyses highly susceptible to contamination from reagents, laboratory environments, and cross-sample carryover [121,122]. Rigorous quality control is essential at every workflow stage. Key methodological considerations and quality-control checkpoints specific to low-biomass urinary microbiome studies are summarized in Table 3.

Sample collection approaches vary by study design. Voided midstream urine minimizes procedural invasiveness but can include perineal contamination; catheterization or suprapubic aspiration reduces external microbial carryover but introduces instrumentation artifacts and patient discomfort. Critically, the sampling method must be explicitly reported and consistent within cohorts to enable cross-study comparison. Regardless of method, samples should be processed within 2 h or immediately frozen at −80°C. DNA extraction must incorporate multiple negative controls (reagent blanks, extraction blanks) to enable statistical contamination assessment [123]. Enhanced quantitative urine culture (EQUC) improves detection of fastidious organisms compared with standard cultures [14].

Sequencing platform selection depends on objectives. 16S rRNA amplicon sequencing offers cost-effective community profiling but lacks species resolution; shotgun metagenomics provides strain-level taxonomy and functional annotation but requires higher biomass [11,12]. Bioinformatic pipelines must explicitly address contamination through statistical decontamination methods (eg, the decontam package [123]), which use negative control data to identify likely contaminants. Transparent reporting of negative control results, contaminant removal criteria, and normalization strategies is essential for reproducibility [121,122].

Standardized operating procedures across studies remain lacking, limiting comparability. International consortia should establish unified guidelines for sample handling, sequencing platforms, and minimum reporting standards [112,124]. Development of rapid molecular assays targeting clinically relevant signatures will facilitate clinical adoption [110,111].

FUTURE DIRECTIONS:

The lack of standardized operating procedures and quality control frameworks in microbiome research limits the comparability of findings across studies. Unified standards are needed across all steps – from sample collection, storage, and processing to sequencing and data analysis. The regulatory approval pathways for microbiome-related products and technologies remain undefined, constituting a major barrier to their clinical implementation. In addition, the high cost of microbiome testing and interventions poses a significant obstacle to broader clinical use. High-throughput sequencing, complex bioinformatic analyses, and individualized intervention strategies all require substantial resource investment. There is a need to develop rapid, marker gene-based assays targeting specific microbial signatures to reduce costs and improve clinical accessibility. Although these studies are promising efforts to improve acute rejection monitoring, larger multicenter prospective trials are needed to provide high-quality evidence.

Future priorities should include establishing standardized frameworks for microbiome research, conducting large-scale validation studies, identifying the most clinically meaningful microbial biomarkers, developing microbiome-based risk prediction models, and validating their effectiveness in rigorously controlled clinical trials, and dedicated regulatory guidelines are needed to provide a clear approval pathway for microbiome-related products. By deepening our understanding of microbiome composition and function, further strategies can be developed to stabilize the post-transplant microbiome. Harnessing artificial intelligence algorithms for adaptive interventions may enable real-time, personalized recommendations based on predicted microbiome trajectories.

Conclusions

The urinary microbiome (urobiome) is increasingly recognized as a clinically relevant component of post-transplant biology. Across studies, kidney transplant recipients commonly exhibit reduced urobiome diversity, loss of putatively protective taxa, and enrichment of opportunistic pathogens, and these shifts have been associated with major transplant outcomes, including UTI, acute rejection, and chronic allograft dysfunction (eg, IF/TA). However, most available evidence remains observational and is strongly influenced by low-biomass sampling, contamination control, peri-transplant antibiotics, and immunosuppressive regimens. Future progress will require standardized collection and reporting frameworks, adequately powered longitudinal cohorts with adjudicated endpoints, and integrative analyses combining microbial profiles with host biomarkers and multi-omics data. With improved reproducibility and validation, the urobiome may contribute to risk stratification, early surveillance, and microbiome-sparing management strategies in kidney transplantation.